Data-driven inference of thermodynamic properties from non-equilibrium stochastic fluctuations

Living systems are driven out of equilibrium via an input of cellular chemical energy, part of which is dissipated into the environment. For systems that exhibit non-equilibrium steady states, thermodynamic fluctuations encode signatures that can be used to infer properties of the system. For example, model-based approaches can be used to quantify how far the system is from equilibrium by measuring the dissipation rate. Here, we present a data-driven approach for inferring system properties based on scattering transforms. The method utilizes symmetries arising from the stationary nature of stochastic fluctuations which allows solving inverse problems with fewer measurements. The results from simulations and experimental data demonstrate that the proposed approach serves as an effective method to infer system properties from thermodynamic fluctuations in living systems.